The Future of Search Engine Techniques • Yoast

In our article “Rethinking SEO in the Age of AI,” we briefly explored how AI could go beyond simple prompt-and-response interactions. A new direction is agent AI. Systems that can take action, not just generate answers. While this area is still evolving, we are already seeing early signs of tools that can identify gaps, suggest improvements, and adapt to changing trends with minimal effort. As these capabilities evolve, they could change the way we think about maintaining ongoing discoverability in SEO.

Understanding the coexistence of web and AI agents

Before we understand agent SEO, let’s first look at the role of AI in shaping the web. Does it stay the same or does it change silently?

It was for a long time The web was more than just a collection of pages. It acted as an interconnected graph of entities. Websites that represent people, companies, ideas and concepts, all connected by content, context and trust. This structure, often referred to as an open network, has remained relatively stable over decades. People created content, users discovered it through search or links, and meaning emerged through exploration.

What seems to be changing now is not the structure itself, but the way the web is accessed and used.

Previously, discovery was largely a direct interaction between people and websites. You searched, clicked, read, compared and came to your own conclusions. Today, AI systems are increasingly taking part in this journey. They sit between the user and the web, interpreting, summarizing, and sometimes even deciding what information is displayed.

This is where the idea of ​​AI agents comes from. Not just as tools that generate answers, but as systems that can navigate the web, retrieve information, and potentially act on it. Early examples, such as experiments with natural language interfaces like NLWeb, point to a web that can be interacted with more dialogically without losing its openness and interconnectedness.

Some refer to this shift as the beginning of an “agent network.” However, it is important to view it less as a complete transformation and more as a layer forming on top of the existing network. The open web still exists, content is still created by people, and links still matter. What is evolving is the way this content is discovered, interpreted and used.

And this shift in interaction is where things get interesting for SEO.

Read more: Yoast is working with Microsoft to help AI understand the Open Web

What does SEO mean on the agent web?

As AI agents begin to redesign the way people interact with the web, that naturally raises a follow-up question: Where is SEO?

For years, SEO has been all about helping users find your content. They optimized for rankings, increased search engine visibility, and relied on users to click, read, and navigate. But if AI agents begin to mediate this journey, not just retrieving information but interpreting and acting on it, then SEO may need to expand its role.

Don’t necessarily replace what already exists, but rather build on it.

From the ranking of pages to selection by systems

In a more agent-driven environment, discoverability may no longer just depend on your ranking, but also on whether your content is selected, trusted, and used by AI systems.

This leads to a subtle but important change:

• It’s not just about being visible
• It’s about being understandable, reliable and usable for machines

AI agents do not surf the Internet like humans. She:

• Analyze structured and unstructured data
• Look for clear signals of authority and accuracy
• Combine information from multiple sources before presenting it

So instead of just optimizing for clicks, SEO can also include optimizing for clicks Integration into AI-generated answers and workflows.

What stays, what develops, what is added

Let’s ground this a little. Traditional SEO isn’t going away. Many of its core principles still apply, but their role may change.

What remains relevant

• High quality, original content
• Clear page structure and internal linking
• Strong technical SEO fundamentals
• Authority and Trust Signals (EEAT)

These remain important as AI systems still rely on the internet as a source of truth.

What is developing

• Keywords → Intent Modeling: It’s less about precisely fitting formulations and more about covering topics in detail and in context
• Rankings → Presence on all surfaces: Visibility can extend beyond SERPs to AI summaries, assistants, and agent output
• Clicks → Influence: Users may not always visit your website, but your content can still influence their decisions

What is added?

• Structured, machine-readable content: Schema, clean formatting and semantic clarity become even more important
• Content designed for extraction: Clear answers, definitions, step-by-step explanations
• Current entity level authority: To be recognized as a trusted source for a topic and not just rank for a keyword
• Freshness and adaptability: Content that evolves as trends and information change

So what does SEO really become?

It looks less like a discipline focused solely on rankings and more like one focused on ongoing discoverability.

Or as Alex Moss puts it in his article The same, but different: Develop your strategy for AI-driven discoverythe web itself could evolve into two parallel experiences:

This has resulted in a split of a completely open web into two parts – the “human” web and the “agentic” web. SEOs must consider both sides of the web and know how to serve both.

This framing makes the change more clear.

Your content still needs to rank. But it also needs to work on a second level of the web, where AI systems interpret, select and sometimes act on information before a human ever sees it.

So your content must be:

• Understood without a doubt
• Trustworthy enough to be used as a reference
• Well structured enough to be reused

In this sense, SEO does not disappear in an agent web. It stretches.

From helping users find information…

to help systems choose.

Role of agent AI in SEO

As the web gradually becomes experienced by both humans and AI agents, it is worth asking what role these agents might potentially play in SEO itself. Not as a replacement for SEO teams, but as a new layer within the way SEO work is done.

What we are starting to see is a shift from SEO as a series of regular tasks to something more continuous, supported and adaptable. Some early tools already indicate this. Not only do they analyze data, they also suggest actions. In some cases they even make changes. If this trend continues, agent AI could become less of a tool you use and more of a system you collaborate with.

Let’s break down where this role could take shape.

How agentic AI can transform SEO workflows

layer	Traditional SEO approach (as it usually works today)	With agentic AI (emerging direction)
Audits → Always-on optimization	SEO teams conduct audits at set intervals (monthly, quarterly) using tools such as site crawlers. Issues like broken links, missing metadata, or slow pages are identified and then manually fixed over time. Improvements often depend on when the audit is conducted.	Systems continually monitor site performance, flag issues as they arise, and can suggest or implement fixes in real time. Optimization occurs continuously and no longer relies on manually scheduled audits.
React → anticipate	Actions are usually triggered by visible changes. For example, a drop in rankings leads to an investigation, or an algorithm update leads to content revisions. SEO is often a reaction to what has already happened.	AI systems analyze patterns in search behavior and performance data to identify early signals. This can mean identifying emerging issues, changing intentions, or withdrawing commitment before it significantly impacts performance.
Manual execution → Guided systems	Tasks like keyword research, clustering, content optimization, and internal linking are performed manually or with tools. SEO specialists interpret the data and make changes step by step.	AI helps you with these tasks by identifying keyword opportunities, grouping topics, suggesting optimizations, and even making specific changes. SEOs are increasingly oriented toward managing strategies, reviewing results, and setting priorities.
Static content → Adaptive content	Content is created, published and revisited from time to time. Updates are often triggered by performance degradation, outdated information, or scheduled content refresh cycles.	Content develops more dynamically. Systems can recommend updates based on performance, refine sections for clarity, or restructure content to better align with user intent and AI consumption patterns.
Generic UX → Contextual Travel	Most users experience the same content and navigation structure. Personalization is limited or rule-based, such as simple recommendations or segmented landing pages.	Experiences become more contextual. Content, navigation, and recommendations can be customized based on user behavior, intent, or journey stage, creating more relevant and engaging interactions.
Technical maintenance → Intelligent infrastructure	Technical SEO includes regular checks for issues such as crawling errors, indexing issues, and schema gaps. Fixes are manually prioritized based on impact and resources.	AI systems continually monitor technical health, automatically prioritize issues, suggest and, in some cases, implement fixes. Structured data, internal links and site architecture can be dynamically optimized.

A quick example: structuring content for machines, not just humans

If agent systems are based on structured, networked and machine-readable content, then this is not completely new territory for SEO.

In many ways, we have already moved in this direction through structured data and schemas. What changes is how important and fundamental it can become.

For example, features like schema aggregation in Yoast SEO bring together different pieces of structured data on a website and connect them into a more unified graph. Instead of treating pages as isolated entities, they help search engines better understand how entities, content types, and relationships fit together.

This may seem like a technical detail, but it reflects a broader change.

As AI agents analyze, combine, and interpret content from multiple sources, clarity and connection at the data level become more important. Not just for visibility in search results, but also for the way content is understood and reused.

While agent AI feels like a new level, some of the fundamental work, like structuring content, defining entities, and building semantic relationships, is already part of modern SEO. In this context it only becomes more critical.

So where does that leave SEO teams?

If there is a pattern to all this, it is not replacement but redistribution.

Agentic AI can do the following:

• Recurring tasks
• Data-intensive analysis
• Continuous monitoring

This allows people to focus more on brand-building aspects such as:

• Strategy and positioning
• Editorial judgment and brand voice
• Decide what should be done, not just what can be done

In this sense, agent AI doesn’t redefine SEO overnight. But it is starting to change the way it is practiced.

Understand the risks and challenges of agent AI for SEO

So far, agent AI may sound like a natural evolution of search engine optimization. But as with most technological changes, there can be trade-offs.

Not because the technology is inherently problematic, but because it introduces new dependencies, new levels of complexity, and new decisions for SEO teams. In this sense, adopting agent AI is not just about adding a new feature. It may also be about rethinking how much control should be delegated and where human judgment continues to play a crucial role.

Here are some of the challenges that could arise as this area evolves:

1. High technical and integration complexity

Agent systems are unlikely to function in isolation. They may need to connect to your CMS, analytics tools, and multiple data sources.

This could lead to challenges such as:

• Managing cross-platform integrations
• Ensuring consistent and reliable data flow
• Define clear workflows across systems

For many teams, this may not be plug-and-play. It might take time, experimentation and coordination between different roles.

2. Data quality and dependency

Agentic AI can be heavily dependent on the quality of the data received. If the data is:

• Outdated
• Incomplete
• Poorly structured

Then the results could reflect these gaps.

On a large scale, even small inconsistencies can influence multiple recommendations or decisions. For this reason, maintaining clean, reliable data sources can become even more important in an agent-driven setup.

3. Risk amplification and the need for governance

One of the strengths of agent AI is speed. But that same speed could also amplify unintended outcomes.

Without clear guardrails:

• Content updates may result in inaccuracies
• Technical changes can lead to problems such as broken links or indexing errors
• Best practices may not always be followed consistently

This is where governance frameworks and approval checkpoints can be crucial, not to slow things down but to keep things moving.

4. Hallucinations and accuracy considerations

AI systems can sometimes produce results that sound plausible but are not entirely accurate.

In an SEO context, this might look like this:

• Misinterpreted data
• Inaccurate keyword insights
• Made up or mixed information

The challenge is that these results are difficult to see at first glance. This suggests that validation and source checking may remain an ongoing part of the workflow.

5. Limited understanding of nuances

SEO often goes beyond data and structure. This includes tone, context and intent. Agent systems may not always fully capture the following:

• Brand voice and positioning
• Legal or compliance nuances
• Subtle differences in user intent

This could result in the results being technically sound but not always contextual. Human input is likely to play a key role here.

6. Balance between automation and human judgment

A broader question that might arise is how much to automate.

• Too much automation could: Reduce control over strategy or brand
• Too little could: Limit efficiency and scalability

Most teams may be able to balance both. Using agent AI to expand their capabilities while driving direction and decision-making.

7. High initial investment and learning curve

Although agent systems may provide long-term efficiencies, it may take some time to get started. This may include:

• Learn how the systems work
• Setting up workflows and integrations
• Align results with business goals

There is also a certain degree of uncertainty here. The technology is still evolving, as are the tools built upon it. This means that costs, capacities and best practices may continue to change.

For many teams, adoption may not be immediate. This could be done gradually, through testing, iteration, and finding out what actually works in practice.

8. Zero-click experiences and changing traffic patterns

As AI systems become increasingly involved in information gathering, zero-click experiences may become more common.

Users could:

• Get answers directly via AI interfaces
• Interact without visiting the original source

This doesn’t necessarily reduce the importance of SEO, but it can change the way success is measured. Visibility and influence could become as relevant as traffic.

What might discoverability look like in an agent-driven web?

Agentic AI potentially opens up new possibilities for performing SEO. In addition, there may also be new considerations.

It might require:

• Stronger data foundations
• Clear governance and review processes
• A thoughtful balance between automation and human input

In many ways, the goal may not be full automation. It could be a better collaboration.

Even as agents take on more executive tasks, responsibility for leadership, accuracy, and trust likely remains with humans. And perhaps that’s the more interesting change here. Not whether AI agents will “take over” SEO, but how they might change what good SEO looks like.

When discoverability is no longer just about ranking, but also about being selected, interpreted and reused by systems, the role of SEO begins to grow. It’s less about optimizing for a single interface and more about preparing content to exist on multiple levels of the web.

So the question is not just:

“How do we rank?”

It could be slow:

• How do you stay understandable across multiple LLMs?
• Do we remain trustworthy enough to be referenced?
• How do we design content that works for both people and machines?

We don’t have all the answers yet. And maybe that’s okay.

Because this is not a fixed goal. It is something that is still taking shape.

And while this is the case, SEO can evolve alongside it. Not disappearing, not being replaced, but adapting to a web that is becoming more dynamic, more diverse and a little less predictable.

Ahad Qureshi

I’m studying computer science and got into writing by chance – and stayed because I fell in love with it. For the last six years, I’ve been immersed in SEO and technical content, turning jargon into stories that actually make sense. When I’m not writing, I’m probably lifting weights to balance my love of food (because yes, the gym and biryani can coexist) or catching up with friends over a nice cup of chai.

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